Integrating Genetic Algorithms and Geographic Information System to Optimize Highway Alignments

Abstract

A comprehensive highway cost model can be used for optimizing highway alignments subject to a number of design constraints. Because a geographic information system (GIS) can spatially represent the locations of properties, floodplains, streams, and other geographical characteristics of significance in a highway cost model, it can provide valuable input to a highway design optimization model. Additionally, a GIS-based model can be developed to compute geographically sensitive costs to be used with an iterative optimization scheme. However, connecting a GIS to a highway optimization model requires full automation of an entire search process in which there is a continuous exchange of inputs and outputs until the optimized solution is obtained. An integrated model is developed by linking a GIS model with an optimization model employing genetic algorithms (GAs). The GIS model provides accurate geographical features, computes location-dependent costs, and transmits these costs to an external program. That program computes length-dependent costs and user costs and then, using GAs, optimizes the highway alignment to minimize the sum of all costs. An example study using real land use and environmental features is presented for a part of Talbot County, Maryland. The computational performance of the integrated model is assessed.

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